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The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach.

Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for ge...

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Published in:Land Economics Vol. 84; no. 2; pp. 241 - 267
Main Authors: Partridge, Mark D., Rickman, Dan S., Ali, Kamar, Olfert, M. Rose
Format: Article
Published: University of Wisconsin Press May 2008
Subjects:
Mathematical models of urban growth
Geography > Methodology
Geography > Statistical methods
Regression analysis
Mathematical geography
United States
Online Access:View this record in EBSCOhost
Summary:Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for geographic heterogeneity in the growth parameters and compare them to global regression estimates. The results indicate significant heterogeneity in the regression coefficients across the country, most notably for amenities and college graduate shares. Using GWR also exposes significant local variations that are masked by global estimates suggesting limitations of a one-size-fits-all approach to describe growth and to inform public policy. Reprinted by permission of the publisher.